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Compare Entities

compare_entities
Read-onlyIdempotent

"Compare X and Y" / "X vs Y" / "X versus Y" / "which is bigger / better / larger / more profitable" / "rank these companies" / "head to head" — side-by-side comparison of 2–5 companies or drugs in ONE parallel call. ALWAYS PREFER over sequential single-pack lookups when comparing entities. type="company" pulls LATEST 10-K revenue + net income + cash + long-term debt from SEC EDGAR/XBRL (off-calendar fiscal years handled correctly — AAPL Sep, NVDA Jan, etc.). type="drug" pulls FAERS adverse-event counts, FDA approval counts, active trial counts. Results sorted by primary metric so "largest" / "most" / "biggest" reads off the top of the response. Returns paired data + pipeworx:// citation URIs per entity. Replaces 8–15 sequential lookups.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type: "company" or "drug".
valuesYesFor company: 2–5 tickers/CIKs (e.g., ["AAPL","MSFT"]). For drug: 2–5 names (e.g., ["ozempic","mounjaro"]).

TDQS

A4.9/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations indicate read-only, idempotent, and non-destructive behavior. Description adds detail about what data is fetched (10-K metrics for companies, FAERS/trial data for drugs), sorting behavior, and citation URIs. No contradictions; description enriches the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is fairly long but every sentence adds value. It front-loads with example queries and key usage instruction. Could be slightly trimmed without losing clarity, but current length is justified by the depth of information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite no output schema, description clearly states what is returned (paired data + citation URIs) and explains sorting. Covers both entity types and addresses behavioral expectations (e.g., off-calendar fiscal year handling). Leaves no significant gaps for a comparison tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but description adds critical context: explains 'type' enum values map to specific data sources (SEC EDGAR/XBRL for companies, FAERS/FDA for drugs), clarifies 'values' as tickers/CIKs or drug names, and notes max/min constraints. Goes beyond schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the tool performs side-by-side comparisons of 2–5 companies or drugs. It provides example user queries and distinguishes itself from single-entity lookups like entity_profile. The verb+resource pairing is specific and unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says 'ALWAYS PREFER over sequential single-pack lookups when comparing entities,' giving clear when-to-use guidance. Provides example phrases and outlines the scenarios for each type (company vs. drug). No confusion about alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.6/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and discover_tools all serve general data querying, while bet_research, polymarket_edges, polymarket_arbitrage, and polymarket_edge_tracker all target prediction-market opportunities. An agent would struggle to pick the right one consistently despite long descriptions.

Naming Consistency2/5

Naming conventions are mixed: some tools use snake_case bare verbs (remember, recall, forget), some use a pipeworx_ prefix, some use tradier_ prefix, and some use descriptive phrases (generate_llms_txt, scan_competitor_ai_presence). There is no consistent verb_noun or prefix pattern across the set.

Tool Count2/5

34 tools is on the heavy side, but more importantly the count does not match the server's stated identity. The server is named Tradier, yet only 3 of 34 tools are Tradier-specific market data tools; the rest are Pipeworx research, prediction-market, memory, and subscription utilities. The scope feels bloated and unfocused.

Completeness1/5

For a server named Tradier, the surface is severely incomplete: only quote, option expirations, and option chain are provided. Missing are account info, positions, orders, historical data, watchlists, and other core brokerage/trading operations. The Pipeworx research side is broad, but the apparent trading domain has major gaps that would cause agent failures.